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Data X:
12 4 10 6 12 7 13 3 1 15 8 12 8 13 12 12 0 1 12 3 12 6 15 8 12 3 0 9 5 11 9 9 6 6 2 0 12 6 7 5 9 10 9 3 1 6 7 11 7 10 7 6 2 1 11 2 12 9 11 10 6 1 0 11 5 6 5 12 10 10 1 1 11 6 10 6 8 8 6 3 0 12 7 11 7 12 12 8 3 1 12 6 10 7 11 12 12 3 1 13 8 13 9 14 12 15 3 1 11 4 10 7 10 12 10 1 1 12 7 11 6 7 10 9 1 1 13 8 14 9 16 14 15 2 1 12 7 12 6 8 10 12 1 1 11 8 12 8 8 7 10 3 0 11 5 11 8 11 11 6 1 0 6 5 9 8 8 7 6 3 0 6 4 10 4 8 8 6 3 0 9 4 9 7 8 6 6 2 0 9 4 9 8 9 8 9 2 0 9 4 12 8 7 6 12 2 1 11 4 11 7 10 11 11 1 1 11 8 11 8 11 6 6 1 0 9 3 12 8 7 6 10 3 0 9 3 8 5 8 6 6 1 0 12 5 9 8 15 9 9 2 1 11 7 10 7 13 7 10 3 1 10 4 11 8 11 10 6 3 0 9 4 11 7 10 8 9 2 0 12 4 10 7 16 6 5 1 1 12 7 9 8 14 11 12 1 1 12 6 12 8 13 9 13 3 0 14 6 12 10 10 10 15 0 0 12 4 8 7 8 10 9 2 0 10 7 10 5 7 6 9 2 0 6 4 12 8 16 6 12 2 1 12 8 8 8 13 10 12 2 1 6 7 10 3 13 6 6 2 0 12 4 15 10 6 9 9 1 0 14 8 8 8 10 11 11 1 1 12 5 13 7 16 12 9 1 0 12 4 12 8 12 13 12 2 0 10 2 8 6 5 7 9 3 1 10 8 9 8 13 8 7 2 1 9 3 11 7 10 7 15 0 1 8 2 10 6 10 9 11 1 1 6 4 11 4 10 6 6 1 0 12 6 6 7 8 8 7 1 1 12 6 12 8 12 12 12 0 1 6 4 10 6 13 7 6 1 0 12 5 11 6 10 9 9 3 0 11 8 9 6 10 12 10 2 0 15 6 9 10 13 12 12 0 1 12 7 11 7 9 7 9 1 0 12 8 9 8 9 12 12 1 0 15 10 11 9 12 15 15 2 1 12 5 13 8 16 12 11 1 1 6 5 11 7 12 4 6 2 0 6 6 10 6 6 10 6 0 1 8 6 7 8 10 10 8 1 0 8 4 8 6 9 9 8 0 1 9 4 8 6 11 6 9 2 1 8 5 8 6 11 8 8 2 1 10 5 9 8 9 11 12 2 1 7 4 9 5 8 7 3 3 1 12 6 12 8 12 12 12 1 0 12 7 13 8 8 11 12 1 0 12 7 11 7 9 12 12 3 0 11 6 12 8 8 6 12 3 0 12 8 12 8 8 12 12 3 1 13 9 10 8 12 12 12 0 1 6 8 7 4 8 6 3 2 0 15 9 5 9 12 15 15 2 1 9 6 11 8 12 8 9 1 1 15 5 12 8 13 13 14 2 1 12 7 13 7 12 10 12 0 1 7 2 11 7 12 9 8 3 0 12 8 13 9 13 12 12 3 1 12 4 9 7 6 12 12 2 1 12 5 11 8 10 11 9 1 0 9 4 11 7 10 7 9 1 0 8 5 6 8 8 6 9 3 0 9 4 11 8 8 6 9 3 1 12 6 13 10 12 11 12 1 1 10 6 8 8 8 6 6 1 1 12 7 13 8 16 13 14 2 1 6 3 7 7 7 8 6 0 1 7 7 7 7 7 8 7 0 1 10 8 11 8 8 11 8 0 0 3 2 5 2 4 7 3 0 1 10 6 12 8 11 10 11 0 0 12 5 12 8 12 8 12 1 0 6 5 4 6 4 3 3 1 0 9 6 12 7 10 10 9 1 1 14 9 12 8 15 12 14 2 0 12 6 12 8 8 7 10 2 1 9 5 10 8 11 7 6 3 1 9 5 11 8 10 7 8 2 1 9 4 10 7 7 8 7 3 1 6 7 8 7 12 6 6 0 1 6 7 8 7 12 6 6 0 1 12 4 10 8 16 10 10 2 1 9 4 9 8 13 8 9 2 0 6 4 6 8 9 6 6 2 0 12 8 12 8 16 12 12 3 1 9 6 11 8 10 8 6 2 1 12 4 12 8 10 10 12 2 1 12 8 9 6 12 12 12 0 1 12 8 9 6 14 12 12 1 1 12 4 6 6 7 10 9 3 0 9 6 12 8 13 9 9 3 0 8 7 9 7 12 6 6 2 0 6 4 11 6 11 6 6 2 0 10 5 7 8 13 8 6 2 1 12 8 12 8 14 12 12 0 1 8 6 10 8 8 6 12 1 1 7 6 7 6 10 8 7 3 1 11 5 10 7 12 10 12 1 1 12 4 11 6 8 10 9 3 1 11 6 12 8 8 10 9 1 0 12 7 9 7 8 11 7 1 1 6 4 12 8 8 6 6 2 0 8 5 8 5 14 8 15 1 1 12 5 12 8 10 6 12 0 1 3 2 3 2 8 3 15 2 1 10 8 10 8 9 6 6 1 1 7 4 11 6 4 5 12 2 1 9 4 6 5 13 10 6 1 0
Names of X columns:
enjoy absorbed learning interest community distraction intention smg gender
Endogenous Variable (Column Number)
Categorization
none
none
quantiles
hclust
equal
Number of categories (only if categorization<>none)
Cross-Validation? (only if categorization<>none)
no
no
yes
Chart options
R Code
library(party) library(Hmisc) par1 <- as.numeric(par1) par3 <- as.numeric(par3) x <- data.frame(t(y)) is.data.frame(x) x <- x[!is.na(x[,par1]),] k <- length(x[1,]) n <- length(x[,1]) colnames(x)[par1] x[,par1] if (par2 == 'kmeans') { cl <- kmeans(x[,par1], par3) print(cl) clm <- matrix(cbind(cl$centers,1:par3),ncol=2) clm <- clm[sort.list(clm[,1]),] for (i in 1:par3) { cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='') } cl$cluster <- as.factor(cl$cluster) print(cl$cluster) x[,par1] <- cl$cluster } if (par2 == 'quantiles') { x[,par1] <- cut2(x[,par1],g=par3) } if (par2 == 'hclust') { hc <- hclust(dist(x[,par1])^2, 'cen') print(hc) memb <- cutree(hc, k = par3) dum <- c(mean(x[memb==1,par1])) for (i in 2:par3) { dum <- c(dum, mean(x[memb==i,par1])) } hcm <- matrix(cbind(dum,1:par3),ncol=2) hcm <- hcm[sort.list(hcm[,1]),] for (i in 1:par3) { memb[memb==hcm[i,2]] <- paste('C',i,sep='') } memb <- as.factor(memb) print(memb) x[,par1] <- memb } if (par2=='equal') { ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep='')) x[,par1] <- as.factor(ed) } table(x[,par1]) colnames(x) colnames(x)[par1] x[,par1] if (par2 == 'none') { m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) } load(file='createtable') if (par2 != 'none') { m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x) if (par4=='yes') { a<-table.start() a<-table.row.start(a) a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'',1,TRUE) a<-table.element(a,'Prediction (training)',par3+1,TRUE) a<-table.element(a,'Prediction (testing)',par3+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Actual',1,TRUE) for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) a<-table.element(a,'CV',1,TRUE) for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) a<-table.element(a,'CV',1,TRUE) a<-table.row.end(a) for (i in 1:10) { ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1)) m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,]) if (i==1) { m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,]) m.ct.i.actu <- x[ind==1,par1] m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,]) m.ct.x.actu <- x[ind==2,par1] } else { m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,])) m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1]) m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,])) m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1]) } } print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred)) numer <- 0 for (i in 1:par3) { print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,])) numer <- numer + m.ct.i.tab[i,i] } print(m.ct.i.cp <- numer / sum(m.ct.i.tab)) print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred)) numer <- 0 for (i in 1:par3) { print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,])) numer <- numer + m.ct.x.tab[i,i] } print(m.ct.x.cp <- numer / sum(m.ct.x.tab)) for (i in 1:par3) { a<-table.row.start(a) a<-table.element(a,paste('C',i,sep=''),1,TRUE) for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj]) a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4)) for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj]) a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4)) a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a,'Overall',1,TRUE) for (jjj in 1:par3) a<-table.element(a,'-') a<-table.element(a,round(m.ct.i.cp,4)) for (jjj in 1:par3) a<-table.element(a,'-') a<-table.element(a,round(m.ct.x.cp,4)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') } } m bitmap(file='test1.png') plot(m) dev.off() bitmap(file='test1a.png') plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response') dev.off() if (par2 == 'none') { forec <- predict(m) result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec)) colnames(result) <- c('Actuals','Forecasts','Residuals') print(result) } if (par2 != 'none') { print(cbind(as.factor(x[,par1]),predict(m))) myt <- table(as.factor(x[,par1]),predict(m)) print(myt) } bitmap(file='test2.png') if(par2=='none') { op <- par(mfrow=c(2,2)) plot(density(result$Actuals),main='Kernel Density Plot of Actuals') plot(density(result$Residuals),main='Kernel Density Plot of Residuals') plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals') plot(density(result$Forecasts),main='Kernel Density Plot of Predictions') par(op) } if(par2!='none') { plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted') } dev.off() if (par2 == 'none') { detcoef <- cor(result$Forecasts,result$Actuals) a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goodness of Fit',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Correlation',1,TRUE) a<-table.element(a,round(detcoef,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'R-squared',1,TRUE) a<-table.element(a,round(detcoef*detcoef,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'RMSE',1,TRUE) a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'#',header=TRUE) a<-table.element(a,'Actuals',header=TRUE) a<-table.element(a,'Forecasts',header=TRUE) a<-table.element(a,'Residuals',header=TRUE) a<-table.row.end(a) for (i in 1:length(result$Actuals)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,result$Actuals[i]) a<-table.element(a,result$Forecasts[i]) a<-table.element(a,result$Residuals[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') } if (par2 != 'none') { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'',1,TRUE) for (i in 1:par3) { a<-table.element(a,paste('C',i,sep=''),1,TRUE) } a<-table.row.end(a) for (i in 1:par3) { a<-table.row.start(a) a<-table.element(a,paste('C',i,sep=''),1,TRUE) for (j in 1:par3) { a<-table.element(a,myt[i,j]) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') }
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Raw Input
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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